Keeping track of language: Can monolingual and bilingual infants associate a speaker with the language they speak?
Bibliographic record
Abstract
Abstract Bilingual infants acquire languages in a variety of language environments. Some caregivers follow a one‐person‐one‐language approach in an attempt to not “confuse” their child. However, the central assumption that infants can keep track of what language a person speaks has not been tested. In two studies, we tested whether bilingual and monolingual 5‐, 12‐ and 18‐month‐olds spontaneously form language‐person associations. In both studies, infants were familiarized with a man and a woman, each speaking a different language, and tested on trials where they either spoke the same language or switched to a different language. In Study 1, infants only heard the speaker, and in Study 2, infants saw and heard the speaker. Bilinguals and monolinguals did not look longer for Switch compared to Same trials; there was no evidence in this task that infants form person‐language associations spontaneously. Thus, our results did not support a central assumption of the one‐person‐one‐language approach, although we cannot rule out that infants do form this association in more naturalistic contexts. Highlights This study investigated whether infants keep track of the language a person speaks, a skill that would be especially relevant in bilingual language environments. In a familiarization‐test paradigm, monolinguals and bilinguals aged 5‐, 12‐, and 18‐months did not notice when a person switched languages. The results call in question whether person‐language associations help bootstrap early bilingual language acquisition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".